Pay Less Attention to Function Words for Free Robustness of Vision-Language Models

Qiwei Tian, CHEN HAO LIN, zhengyuzhao, Chao Shen

Pay Less Attention to Function Words for Free Robustness of Vision-Language Models: 2 upvotes on Hugging Face Daily Papers, #20 of 22 papers on 2025-12-11. Day-by-day upvote history.

To address the trade-off between robustness and performance for robust VLM, we observe that function words could incur vulnerability of VLMs against cross-modal adversarial attacks, and propose Function-word De-Attention (FDA) accordingly to mitigate the impact of function words. Similar to differential amplifiers, our FDA calculates the original and the function-word cross-attention within attention heads, and differentially subtracts the latter from the former for more aligned and robust VLMs. Comprehensive experiments include 2 SOTA baselines under 6 different attacks on 2 downstream tasks, 3 datasets, and 3 models. Overall, our FDA yields an average 18/13/53% ASR drop with only 0.2/0.3/0.6% performance drops on the 3 tested models on retrieval, and a 90% ASR drop with a 0.3% performance gain on visual grounding. We demonstrate the scalability, generalization, and zero-shot performance of FDA experimentally, as well as in-depth ablation studies and analysis. Code will be made publicly at https://github.com/michaeltian108/FDA.

Paper page on Hugging Face · arXiv

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